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20162023
most citedDeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

591 citations · 3.3k across the 128 of their papers we have counts for

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Showing 2019Show all

40 papers · 1 filter

cs.CL2019★ 2 cited

Hybrid Machine Learning Models of Classifying Residential Requests for Smart Dispatching

T. Chen, J. Sun, H. Lin +1

This paper presents a hybrid machine learning method of classifying residential requests in natural language to responsible departments that provide timely responses back to reside…

cs.SE2019★ 5 cited

CORE: Automating Review Recommendation for Code Changes

JingKai Siow, Cuiyun Gao, Lingling Fan +2

Code review is a common process that is used by developers, in which a reviewer provides useful comments or points out defects in the submitted source code changes via pull request…

cs.CV2019★ 16 cited

HAMBox: Delving into Online High-quality Anchors Mining for Detecting Outer Faces

Yang Liu, Xu Tang, Xiang Wu +3

Current face detectors utilize anchors to frame a multi-task learning problem which combines classification and bounding box regression. Effective anchor design and anchor matching…

cs.CL2019

Iterative Dual Domain Adaptation for Neural Machine Translation

Jiali Zeng, Yang Liu, Jinsong Su +4

Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.…

cs.CR2019★ 19 cited

Reviewing and Improving the Gaussian Mechanism for Differential Privacy

Jun Zhao, Teng Wang, Tao Bai +7

Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying -differentia…

cs.CL2019★ 10 cited

Learning to Predict Explainable Plots for Neural Story Generation

Gang Chen, Yang Liu, Huanbo Luan +3

Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in imp…